Brain Tumor Detection Along with Symptoms Analysis.

Abstract

Detecting brain tumors early is crucial for improving patient outcomes, but traditional diagnostic methods like MRI can be time-consuming and prone to errors. To address this, researchers have turned to computational intelligence techniques, particularly deep learning, to enhance accuracy and efficiency in tumor diagnosis. This review paper explores the evolution of machine learning methods, focusing on deep learning approaches, for identifying glioma, meningioma, and pituitary gland tumors, as well as healthy brain tissue, using MRI images. While traditional machine learning has shown promise, recent advancements in deep learning offer potential for even great accuracy and robustness. Additionally, this study incorporate a comprehensive risk assessment frame-work, evaluating factors like genetic risk, occupational hazards, and various symptomatic indicator to enhance predictive accuracy and earliest detection. The paper discusses challenges such as parameter optimization and limited data availabilities, while also emphasizing the importance of early tumor detection and outlining the processes involved, including tumor detection, segmentation, and classification. By summarizing key achievements and performance metrics of applied algorithms, this paper serves as a roadmap for future research in the field of brain tumor detection using deep learning techniques.